We experimented with 5 binary classifiers: (1) XGBoost (XGB) [1]} with 100 boosted rounds, unbounded depth, and logistic objective, (2) logistic regression (LR) with \(L_2\)  penalty, \(C=1\) , and LBFGS solver, (3) 2-layer multi-layer perceptron (\(\text{MLP}_S\) ) with ReLU activation [2]}, Adam optimizer [3]}, lr=0.001, 200 max epochs, and 50 units in each hidden layer, (4) \(\text{MLP}_M\)  same as (3) but with 100 units in each layer, and (5) \(\text{MLP}_L\)  same as (3) but with 500 units. We use the XGBoost [1]} library for (1) and scikit-learn [5]} for (2-5). We also explored 3 aggregation functions (agg): concatenation (cat), mean pooling (mean), and pairwise absolute distance (diff).
